Advancement, Fall 1999

Advancement, a supplement to Bostonia magazine, provided updates on BU development activities, including major gifts and projects

The Very High Energy source catalog at the ASI Science Data Center

The increasing number of Very High Energy (VHE) sources discovered by the current generation of Cherenkov telescopes made particularly relevant the creation of a dedicated source catalogs as well as the cross-correlation of VHE and lower energy bands data in a multi-wavelength framework. The "TeGeV Catalog" hosted at the ASI Science Data Center (ASDC) is a catalog of VHE sources detected by ground-based Cherenkov detectors. The TeGeVcat collects all the relevant information publicly available about the observed GeV/TeV sources. The catalog contains also information about public light curves while the available spectral data are included in the ASDC SED Builder tool directly accessible from the TeGeV catalog web page. In this contribution we will report a comprehensive description of the catalog and the related tools.Comment: 5 pages, 3 figures - Proceeding of the 34th International Cosmic Ray Conferenc

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined

Panchromatic spectral energy distributions of Herschel sources

(abridged) Far-infrared Herschel photometry from the PEP and HerMES programs is combined with ancillary datasets in the GOODS-N, GOODS-S, and COSMOS fields. Based on this rich dataset, we reproduce the restframe UV to FIR ten-colors distribution of galaxies using a superposition of multi-variate Gaussian modes. The median SED of each mode is then fitted with a modified version of the MAGPHYS code that combines stellar light, emission from dust heated by stars and a possible warm dust contribution heated by an AGN. The defined Gaussian grouping is also used to identify rare sources. The zoology of outliers includes Herschel-detected ellipticals, very blue z~1 Ly-break galaxies, quiescent spirals, and torus-dominated AGN with star formation. Out of these groups and outliers, a new template library is assembled, consisting of 32 SEDs describing the intrinsic scatter in the restframe UV-to-submm colors of infrared galaxies. This library is tested against L(IR) estimates with and without Herschel data included, and compared to eight other popular methods often adopted in the literature. When implementing Herschel photometry, these approaches produce L(IR) values consistent with each other within a median absolute deviation of 10-20%, the scatter being dominated more by fine tuning of the codes, rather than by the choice of SED templates. Finally, the library is used to classify 24 micron detected sources in PEP GOODS fields. AGN appear to be distributed in the stellar mass (M*) vs. star formation rate (SFR) space along with all other galaxies, regardless of the amount of infrared luminosity they are powering, with the tendency to lie on the high SFR side of the "main sequence". The incidence of warmer star-forming sources grows for objects with higher specific star formation rates (sSFR), and they tend to populate the "off-sequence" region of the M*-SFR-z space.Comment: Accepted for publication in A&A. Some figures are presented in low resolution. The new galaxy templates are available for download at the address http://www.mpe.mpg.de/ir/Research/PEP/uvfir_temp

Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks

The Fermi Large Area Telescope (LAT) is currently the most important facility for investigating the GeV $\gamma$-ray sky. With Fermi LAT more than three thousand $\gamma$-ray sources have been discovered so far. 1144 ($\sim40\%$) of the sources are active galaxies of the blazar class, and 573 ($\sim20\%$) are listed as Blazar Candidate of Uncertain type (BCU), or sources without a conclusive classification. We use the Empirical Cumulative Distribution Functions (ECDF) and the Artificial Neural Networks (ANN) for a fast method of screening and classification for BCUs based on data collected at $\gamma$-ray energies only, when rigorous multiwavelength analysis is not available. Based on our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77 objects remain uncertain. Moreover, radio analysis and direct observations in ground-based optical observatories are used as counterparts to the statistical classifications to validate the method. This approach is of interest because of the increasing number of unclassified sources in Fermi catalogs and because blazars and in particular their subclass High Synchrotron Peak (HSP) objects are the main targets of atmospheric Cherenkov telescopes.Comment: 18 pages, 17 figures, accepted for publication on MNRA

Improving Photometric Redshifts using GALEX Observations for the SDSS Stripe 82 and the Next Generation of SZ Cluster Surveys

Four large-area Sunyaev-Zeldovich (SZ) experiments -- APEX-SZ, SPT, ACT, and Planck -- promise to detect clusters of galaxies through the distortion of Cosmic Microwave Background photons by hot (> 10^6 K) cluster gas (the SZ effect) over thousands of square degrees. A large observational follow-up effort to obtain redshifts for these SZ-detected clusters is under way. Given the large area covered by these surveys, most of the redshifts will be obtained via the photometric redshift (photo-z) technique. Here we demonstrate, in an application using ~3000 SDSS stripe 82 galaxies with r<20, how the addition of GALEX photometry (FUV, NUV) greatly improves the photometric redshifts of galaxies obtained with optical griz or ugriz photometry. In the case where large spectroscopic training sets are available, empirical neural-network-based techniques (e.g., ANNz) can yield a photo-z scatter of $\sigma_z = 0.018 (1+z)$. If large spectroscopic training sets are not available, the addition of GALEX data makes possible the use simple maximum likelihood techniques, without resorting to Bayesian priors, and obtains $\sigma_z=0.04(1+z)$, accuracy that approaches the accuracy obtained using spectroscopic training of neural networks on ugriz observations. This improvement is especially notable for blue galaxies. To achieve these results, we have developed a new set of high resolution spectral templates based on physical information about the star formation history of galaxies. We envision these templates to be useful for the next generation of photo-z applications. We make our spectral templates and new photo-z catalogs available to the community at http://www.ice.csic.es/personal/jimenez/PHOTOZ .Comment: 10 pages, 8 figure